System, non-transitory computer readable medium, and method for personalized complementary recommendations

Patent metadata

Field	Value
Publication number	US-11468494-B2
Application number	US-202017096666-A
Country	US
Kind code	B2
Filing date	Nov 12, 2020
Priority date	Nov 12, 2020
Publication date	Oct 11, 2022
Grant date	Oct 11, 2022

Abstract

Systems and methods for generating a set of personalized complementary recommendations is disclosed. A user identifier and an anchor item identifier are received. A set of personalized-weighted items and a set of complimentary-weighted items are each generated based on the user identifier and the anchor item identifier. The personalized-weighted items are generated by a trained supervised model. The complementary-weighted items are generated by a trained unsupervised model. A set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items is generated.

First claim

What is claimed is:

1. A system, comprising:
a non-transitory memory having instructions stored thereon and a processor configured to read the instructions to:
generate, by an iterative training process using a first training data set including add-to-cart data, a trained supervised model;
generate, by an iterative training process using a second training data set including a triple including a user, an anchor item, and a recommendation item, a trained unsupervised model, wherein the trained unsupervised model is configured to minimize a loss function between at least two vectors in a triple of u,i,j , where u is a vector representation of a user, i is a vector representation of an anchor item, and j is a vector representation of an item co-purchased with the anchor item;
receive a user identifier and an anchor item identifier;
generate, by the trained supervised model, a set of personalized-weighted items based on the user identifier and the anchor item identifier;
generate, by the trained unsupervised model, a set of complementary-weighted items based on the user identifier; and
generate a set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items.

2. The system of claim 1 , wherein the trained supervised model applies a logistic regression to a predetermined set of features to generate the set of personalized-weighted items.

3. The system of claim 2 , wherein the predetermined set of features includes a feature f where:
f u,i,j =Σ u m 1/ r

where m is a number of add-to-cart sequences in which a user u adds an item i to a cart and adds an item j to the cart within a time window k, and r is the position of item j when item j is added to the cart.

4. The system of claim 2 , wherein the predetermined set of features includes a feature representative of a recency of a recommended item bought by a user.

5. The system of claim 1 , wherein the loss function is represented as:
= u,i + u,j + i,j ,u∈U,i∈V a ,j∈V r

where U is vector space of the user, V a , is a vector space of the anchor item, and V r is a vector space of the co-purchased item.

6. The system of claim 1 , wherein the processor is configured to read the instructions to generate the set of personalized complementary recommendations to:
generate a set of ranked complimentary-weighted items by ranking the set of complimentary-weighted items according to at least one metric;
generate a set of combined items including at least one of the personalized-weighted items and at least one of the ranked complimentary-weighted items; and
select k top-ranked items from the set of combined items.

7. The system of claim 6 , wherein the at least one metric comprises a distance between an anchor item i and a recommended item j.

8. A non-transitory computer readable medium having instructions stored thereon, wherein the instructions, when executed by a processor cause a device to perform operations comprising:
generating, by an iterative training process using a first training data set including add-to-cart data, a trained supervised model;
generating, by an iterative training process using a second training data set including a triple including a user, an anchor item, and a recommendation item, a trained unsupervised model, wherein the trained unsupervised model is configured to minimize a loss function between at least two vectors in a triple of u,i,j , where u is a vector representation of a user, i is a vector representation of an anchor item, and j is a vector representation of an item co-purchased with the anchor item;
receiving a user identifier and an anchor item identifier;
generating, by the trained supervised model, a set of personalized-weighted items based on the user identifier and the anchor item identifier;
generating, by the trained unsupervised model, a set of complementary-weighted items based on the user identifier and the anchor item identifier; and
generating a set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items.

9. The non-transitory computer readable medium of claim 8 , wherein the trained supervised model applies a logistic regression to a predetermined set of features to generate the set of personalized-weighted items.

10. The non-transitory computer readable medium of claim 9 , wherein the predetermined set of features includes a feature f where:
f u,i,j =Σ u m 1/ r

where m is a number of add-to-cart sequences in which a user u adds an item i to a cart and adds an item j to the cart within a time window k, and r is the position of item j when item j is added to the cart.

11. The non-transitory computer readable medium of claim 9 , wherein the predetermined set of features includes a feature representative of a recency of a recommended item bought by a user.

12. The non-transitory computer readable medium of claim 8 , wherein the loss function is represented as:
= u,i + u,j + i,j ,u∈U,i∈V a ,j∈V r

where U is vector space of the user, V a , is a vector space of the anchor item, and V r is a vector space of the co-purchased item.

13. The non-transitory computer readable medium of claim 8 , wherein generating the set of personalized complementary recommendations comprises:
generating a set of ranked complimentary-weighted items by ranking the set of complimentary-weighted items according to at least one metric;
generating a set of combined items including at least one of the personalized-weighted items and at least one of the ranked complementary-weighted items; and
selecting k top-ranked items from the set of combined items.

14. The non-transitory computer readable medium of claim 13 , wherein the at least one metric comprises a distance between an anchor item i and a recommended item j.

15. A computer-implemented method, comprising:
generating, by an iterative training process using a first training data set including add-to-cart data, a trained supervised model;
generating, by an iterative training process using a second training data set including a triple including a user, an anchor item, and a recommendation item, a trained unsupervised model, wherein the trained unsupervised model is configured to minimize a loss function between at least two vectors in a triple of u,i,j , where u is a vector representation of a user, i is a vector representation of an anchor item and j is a vector representation of an item co-purchased with the anchor item, and wherein the loss function is represented as:
= u,i + u,j + i,j ,u∈U,i∈V a ,j∈V r

where U is vector space of the user, V a is a vector space of the anchor item, and V r is a vector space of the co-purchased item; and
receiving a user identifier and an anchor item identifier;
generating a set of personalized-weighted items, by the trained supervised model, based on the user identifier and the anchor item identifier;
generating a set of complementary-weighted items, by the trained unsupervised model, based on the user identifier and the anchor item identifier; and
generating a set of personalized complementary recommendations including a subset of the personalized-weighted items and a subset of the complementary-weighted items.

16. The computer-implemented method of claim 15 , wherein the trained supervised model applies a logistic regression to a predetermined set of features to generat

Assignees

• Walmart Apollo Llc

Inventors

• Sinha Nimesh
• Ma Luyi
• Cho Hyun Duk
• Kumar Sushant
• Achan Kannan
• Ramkumar Rahul

Classifications

• G06N20/00

  Machine learning · CPC title

• G06Q30/0631 Primary

  Recommending goods or services · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06Q30/0629 Primary

  by pre-processing results, e.g. ranking or ordering results · CPC title